BAYESIAN TECHNIQUE (MEDICAL IMAGE SEGMENTATION) FOR IMAGE CLASSIFYING REGISTRATION

I n this paper, we propose a new model in which the similarity criterion is adapted locally to images by classification of image intensity dependencies. Defined in a Bayesian framework, the similarity criterion is a mixture of probability distributions desc ribing dependencies on two classes. The model also includes a class map which locates pixels of the two classes and weights the two mixture components. The registration problem is formulated both as an energy minimization problem and as a Maximum A Posteri ori (MAP) estimation problem. It is solved using a gradient descent algorithm. In the problem formulation and resolution, the image deformation and the class map are estimated at the same time, leading to an original combination of registration and classif ication that we call image classifying registration. Finally, we illustrate the interest of our model on two real applications from medical imaging: template - based segmentation of contrast - enhanced images and lesion detection in mammograms. We also conduct an evaluation of our model on simulated medical data and show its ability to take into account spatial variations of intensity dependencies while keeping good registration accuracy.